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{#fig-plt-eval-om fig-pos=H}
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{#fig-plt-eval-pr fig-pos=H}
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> I received a response from the editorial board. In short, you can go ahead and submit it to the Special Issue. Let me know if you'd like me to review the title page and the anonymised manuscript-those are the only two unique documents you'll need for the submission. As mentioned in my previous email, you can upload all supplementary materials and replication files to OSF and include the anonymised link in both the manuscript and the title page. If you'd like to meet, I'm happy to briefly walk you through the process.
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>
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> Best,
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> Alex
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@@ -44,7 +44,7 @@ But first, a closer look at the underlying issues leading to the recent developm
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# Background
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# Background
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In his widely reviewed standard reading "Seven rules for social research", @4ff8afa9-5c92-3c50-b832-a1756ccbeedc emphasizes the importance of the reproduction of research findings. But already in the title of the chapter or the rule itself, Firebaugh cuts back on his appeal: "replicate *where possible*". Emphasizing the increasing availability of data, he acknowledges the challenges researchers face in achieving true replication and advertises optimism. As the book is from 2008 and the acceptance of the book is at least perceived to be high, one could expect that replication today as well as research practices enabling replication are broadly adopted. But is this the case?
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In his widely reviewed standard reading "Seven rules for social research", @4ff8afa9-5c92-3c50-b832-a1756ccbeedc emphasizes the importance of the reproduction of research findings. But already in the title of the chapter or the rule itself, Firebaugh cuts back on his appeal: "replicate *where possible*". He notes increasing data availability, yet acknowledges challenges for true replication. Given the books influence since 2008, one might expect replication and replication-enabling practices to be widely adopted today. But is this the case?
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Besides the theoretically driven discourse, there are quite tangible reasons to talk about the scientific method, replication and the publication process. Analyzing 77 research teams assessing the same dataset for a single hypothesis, @breznauObservingManyResearchers2022 found extremely diverse results, ranging from strong positive to strong negative outcomes. They termed this phenomenon "researcher degrees of freedom", explaining that most of the variance in results was not explained by assigned conditions, research decisions, or researcher characteristics. Instead, idiosyncratic researcher variability accounted for more than 90% of the variance.
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Besides the theoretically driven discourse, there are quite tangible reasons to talk about the scientific method, replication and the publication process. Analyzing 77 research teams assessing the same dataset for a single hypothesis, @breznauObservingManyResearchers2022 found extremely diverse results, ranging from strong positive to strong negative outcomes. They termed this phenomenon "researcher degrees of freedom", explaining that most of the variance in results was not explained by assigned conditions, research decisions, or researcher characteristics. Instead, idiosyncratic researcher variability accounted for more than 90% of the variance.
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@@ -70,19 +70,15 @@ All the above leads to the conclusion, that our institutions make refutation har
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## Open Science Practices
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## Open Science Practices
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Following an extensive literature review @vicente-saezOpenScienceNow2018a characterize OS using four differentias: transparency in communication, accessibility or searchability to all data and materials, sharing of everything with a commitment to do so and collaboration along a scientific, distributed global dialogue throughout all stages involved in science. They integrate these into a succinct definition: "Open Science is transparent and accessible knowledge that is shared and developed through collaborative networks" [@vicente-saezOpenScienceNow2018a, p. 434]
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Following an extensive literature review @vicente-saezOpenScienceNow2018a characterize OS using four differentias: transparency in communication, accessibility or searchability to all data and materials, sharing of everything with a commitment to do so and collaboration along a scientific, distributed global dialogue throughout all stages involved in science. They integrate these into a succinct definition: "Open Science is transparent and accessible knowledge that is shared and developed through collaborative networks" [@vicente-saezOpenScienceNow2018a, p. 434]. @banksAnswers18Questions2019 establish a broader definition of os that refers to many concepts, including scientific philosophies embodying communality and universalism, specific practices operationalizing these norms including os policies. A common ground is that *open* science and OSPs try to prevent research misconduct by simply increasing research transparency.
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@banksAnswers18Questions2019 establish a broader definition of os that refers to many concepts, including scientific philosophies embodying communality and universalism, specific practices operationalizing these norms including os policies, like sharing of data and analytic files, redefinition of confidence thresholds, preregistration of studies and analytical plans, engagement in replication studies, removal of pay-walls, incentive systems to encourage the above practices and even specific citation standards. A common ground is that *open* science and OSPs try to prevent research misconduct by simply increasing research transparency [@banksAnswers18Questions2019].
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Building on these definitions, in line with the work of many other authors from diverse disciplines [e.g. @dienlinAgendaOpenScience2021; and @greenspanOpenSciencePractices2024], there are numerous practices that have been proposed to enact OS.
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Building on these definitions, in line with the work of many other authors from diverse disciplines [e.g. @dienlinAgendaOpenScience2021; and @greenspanOpenSciencePractices2024], there are numerous practices that have been proposed to enact OS. The most discussed will be evaluated in the next sections.
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### Open Data and Open Materials
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### Open Data and Open Materials
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*Open data* and *open materials* both enable replication publishing all materials necessary to reproduce research in detail, finding errors, bias or simply support the results of the replicated work [@dienlinAgendaOpenScience2021]. While open data reduces p-hacking, facilitates new research by enabling reproduction, reveals mistakes in the analytical code and enables a diffusion of knowledge on the research process, it seems that many scientists, journals and other institutions start to adopt open data in their research to an increasing extent [@finkReplicationCodeAvailability2024; @freeseAdvancesTransparencyReproducibility2022; @zenk-moltgenFactorsInfluencingData2018].
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*Open data* and *open materials* enable replication by publishing all materials necessary to reproduce research in detail, finding errors, bias or simply support the results of the replicated work [@dienlinAgendaOpenScience2021].
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**Open data** (OD) is defined as *the sharing of data that was collected, generated or obtained from a third party and processed to investigate the research question assessed in the publication*.
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**Open data** (OD) is defined as *the sharing of data that was collected, generated or obtained from a third party and processed to investigate the research question assessed in the publication*. Open materials are often shared alongside open data. To delineate a differentiated picture as sharing behavior for data and materials can be expected to differ due to for example privacy concerns, **open materials** (OM) are distinctively defined as *all research materials necessary to reproduce the reported results like notebooks, code or syntax, guides, protocols that can be shared digitally*. Both definitions closely follow the definitions given by the @americanpsychologicalassociationOpenScienceBadges.
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Open materials are often shared alongside open data. To delineate a differentiated picture as sharing behavior for data and materials can be expected to differ due to for example privacy concerns, **open materials** (OM) are distinctively defined as *all research materials necessary to reproduce the reported results like notebooks, code or syntax, guides, protocols that can be shared digitally*. Both definitions closely follow the definitions given by the @americanpsychologicalassociationOpenScienceBadges.
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First, there is accumulating evidence that providing data alongside publications increases visibility and impact. Some estimates suggest around a 30% citation increase for papers that share data, and importantly, this advantage appears at least partly independent of JIF [@tennantAcademicEconomicSocietal2016; @banksAnswers18Questions2019]. Beyond citations, openly available datasets enable the exploration by others, supporting novel findings and exploratory, hypothesis-generating work [@piwowarSharingDetailedResearch2007; @piwowarStateOALargescale2018].
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First, there is accumulating evidence that providing data alongside publications increases visibility and impact. Some estimates suggest around a 30% citation increase for papers that share data, and importantly, this advantage appears at least partly independent of JIF [@tennantAcademicEconomicSocietal2016; @banksAnswers18Questions2019]. Beyond citations, openly available datasets enable the exploration by others, supporting novel findings and exploratory, hypothesis-generating work [@piwowarSharingDetailedResearch2007; @piwowarStateOALargescale2018].
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Finally, openness has economic and societal benefits, even more evident for open access. It discourages redundant data collection, enabling cost savings that can be redirected to new research questions [@tennantAcademicEconomicSocietal2016; @piwowarSharingDetailedResearch2007]. At the same time, the public availability of data stimulates methodological innovation and cross-dataset syntheses that would otherwise remain infeasible [@piwowarStateOALargescale2018]. These dynamics amplify the academic, economic, and societal impact of research [@tennantAcademicEconomicSocietal2016].
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Finally, openness has economic and societal benefits, even more evident for open access. It discourages redundant data collection, enabling cost savings that can be redirected to new research questions [@tennantAcademicEconomicSocietal2016; @piwowarSharingDetailedResearch2007]. At the same time, the public availability of data stimulates methodological innovation and cross-dataset syntheses that would otherwise remain infeasible [@piwowarStateOALargescale2018]. These dynamics amplify the academic, economic, and societal impact of research [@tennantAcademicEconomicSocietal2016].
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Despite these gains, legitimate concerns persist among many researchers. With increasingly powerful linkage and inference techniques, even 'anonymized' datasets can risk re-identification if insufficient safeguards are in place. Researchers may fear that openness exposes flaws, invites reputational harm, or enables misuse-but detecting and correcting errors is core to good scientific practice and should be actively encouraged [@banksAnswers18Questions2019]. A major practical barrier is time and effort. Preparing shareable assets-de-identifying data, curating metadata, writing codebooks, cleaning and packaging analysis code-can be complex and resource-intensive [@loggPreregistrationWeighingCosts2021; @sarafoglouSurveyHowPreregistration2022]. While many researchers see challenges in the publication of their data and materials, many of these concerns could be ruled out by streamlined processes or institutional support [@freeseAdvancesTransparencyReproducibility2022; @freeseReplicationStandardsQuantitative2007; @americanpsychologicalassociationOpenScienceBadges].
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Despite these gains, legitimate concerns persist among many researchers. With increasingly powerful linkage and inference techniques, even 'anonymized' datasets can risk re-identification if insufficient safeguards are in place. Researchers may fear that openness exposes flaws, invites reputational harm, or enables misuse - but detecting and correcting errors is core to good scientific practice and should be actively encouraged [@banksAnswers18Questions2019]. A major practical barrier is time and effort. Preparing shareable assets such as de-identifying data, curating metadata and documented code, can be complex and resource-intensive [@loggPreregistrationWeighingCosts2021; @sarafoglouSurveyHowPreregistration2022]. While many researchers see challenges in the publication of their data and materials, many of these concerns could be ruled out by streamlined processes or institutional support [@freeseAdvancesTransparencyReproducibility2022; @freeseReplicationStandardsQuantitative2007; @americanpsychologicalassociationOpenScienceBadges].
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There are also method-specific hurdles. For qualitative research, transparency and data sharing can be especially challenging when meaning-making is relational and context-dependent. Fieldnotes and transcripts may lose essential value once separated from the researcher and participants [@breznauDoesSociologyNeed2021; @freeseReplicationSocialScience2017]. These issues underscore that one-size-fits-all mandates are unlikely to succeed.
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There are also method-specific hurdles. For qualitative research, transparency can be especially challenging when meaning-making is relational and context-dependent. Fieldnotes and transcripts may lose essential value once separated from the researcher and participants [@breznauDoesSociologyNeed2021; @freeseReplicationSocialScience2017]. These issues underscore that one-size-fits-all mandates are unlikely to succeed.
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In short, many systemic and researcher-centric challenges cut across OSPs-and they will reappear in the discussion of preregistration that follows.
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In short, many systemic and researcher-centric challenges cut across OSPs - and they will reappear in the discussion of preregistration that follows.
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### Preregistration
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### Preregistration
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A preregistration is a time-stamped plan for a study's hypotheses, design, and analysis, often made public. Its contents vary by method (e.g., hypotheses, sampling, interview guides, exclusion rules, analysis plans) [@loggPreregistrationWeighingCosts2021; @managoPreregistrationRegisteredReports2023; @americanpsychologicalassociationOpenScienceBadges].
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A preregistration is a time-stamped plan for a study's hypotheses, design, and analysis, often made public. Its contents vary by method (e.g., hypotheses, sampling, interview guides, exclusion rules, analysis plans) [@loggPreregistrationWeighingCosts2021; @managoPreregistrationRegisteredReports2023; @americanpsychologicalassociationOpenScienceBadges].
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Timestamping restrains HARKing by separating predictions from evidence, reducing the flexibility for post-hoc theorizing [@scogginsMeasuringTransparencySocial2024; @loggPreregistrationWeighingCosts2021]. More broadly, by committing ex ante, researcher degrees of freedom are narrowed: the analytic and design choices that otherwise enable selective reporting or specification searching are constrained, and any deviations become visible to readers and reviewers. The same logic limits p-hacking: when transformations, outlier rules, model families, covariates, and confirmatory contrasts are specified in advance, cherry-picking becomes less feasible because analytical decisions are made independently of the data. Preregistration also addresses structural issues of study quality. Declaring sample-size requirements upfront helps prevent underpowered designs by construction [@kuhbergerPublicationBiasPsychology2014; @grossmannOpenScienceReform2021]. We predefine theory, measures, and analyses, seek early input, and document choices so reviewers can vet them and avoid misinterpretation-strengthening credibility [ @evansImprovingEvidencebasedPractice2023; @sarafoglouSurveyHowPreregistration2022; @scogginsMeasuringTransparencySocial2024]. Preregistration helps separate confirmatory from exploratory work, reduces publication bias (e.g., via Registered Reports), and narrows "researcher degrees of freedom" [@simmonsFalsePositivePsychologyUndisclosed2011].
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Timestamping restrains HARKing by separating predictions from evidence, reducing the flexibility for post-hoc theorizing [@scogginsMeasuringTransparencySocial2024; @loggPreregistrationWeighingCosts2021]. More broadly, by committing ex ante, researcher degrees of freedom are narrowed. The analytic and design choices that otherwise enable selective reporting or specification searching are constrained, and any deviations become visible to readers and reviewers. The same logic limits p-hacking: when transformations, outlier rules, model families, covariates, and confirmatory contrasts are specified in advance, cherry-picking becomes less feasible because analytical decisions are made independently of the data. Preregistration also addresses structural issues of study quality. Declaring sample-size requirements upfront helps prevent underpowered designs by construction [@kuhbergerPublicationBiasPsychology2014; @grossmannOpenScienceReform2021]. We predefine theory, measures, and analyses, seek early input, and document choices so reviewers can vet them and avoid misinterpretation-strengthening credibility [ @evansImprovingEvidencebasedPractice2023; @sarafoglouSurveyHowPreregistration2022; @scogginsMeasuringTransparencySocial2024]. Preregistration helps separate confirmatory from exploratory work, reduces publication bias (e.g., via Registered Reports), and narrows "researcher degrees of freedom" [@simmonsFalsePositivePsychologyUndplanned, what changed, and why-we reduce bias, improve interpretability, and isclosed2011].
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For this work, **preregistration** is defined as *the act of planning and documenting the hypotheses, study design, and analysis plan of a study before data is collected or even viewed. The documentation is typically time-stamped and made publicly available*.
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For this work, **preregistration** is defined as *the act of planning and documenting the hypotheses, study design, and analysis plan of a study before data is collected or even viewed. The documentation is typically time-stamped and made publicly available*.
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The Open Science movement, particularly preregistration, has been criticized for not providing tailored transparency practices for qualitative research and for importing a positivist framework that may not fit all traditions [@breznauDoesSociologyNeed2021]. Nevertheless, the core principle of transparency remains relevant: qualitative reports should contain enough information for another researcher to understand the logic and process behind the findings [@breznauDoesSociologyNeed2021]. In qualitative contexts, preregistration can focus on documenting guiding questions, sampling logic, coding frameworks, and decision trails while remaining compatible with iterative analysis.
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The Open Science movement, particularly preregistration, has been criticized for not providing tailored transparency practices for qualitative research and for importing a positivist framework that may not fit all traditions [@breznauDoesSociologyNeed2021]. Nevertheless, the core principle of transparency remains relevant: qualitative reports should contain enough information for another researcher to understand the logic and process behind the findings [@breznauDoesSociologyNeed2021]. In qualitative contexts, preregistration can focus on documenting guiding questions, sampling logic, coding frameworks, and decision trails while remaining compatible with iterative analysis.
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Frequently voiced concerns are about increasing work, thereby lengthening projects and restricting researcher's freedom by confining them to their predefined plan. However, those are misconceptions. Preplanning simply reorders the workflow rather than creating extra work. This can prevent costly redesigns or follow-up-studies. Additionally it does not inhibit exploratory work, the goal is to provide clarity and transparency by distinguishing between preplanned analysis and those conducted after viewing the data. By moving the conceptual work upstream, preregistration clarifies claims, adds transparency to the decision process and strengthens credibility by marking plans and deviations [@loggPreregistrationWeighingCosts2021; @evansImprovingEvidencebasedPractice2023]. In-principle acceptance adds a guarantee to the upfront work, provided the approved plan is followed[@sarafoglouSurveyHowPreregistration2022; @banksAnswers18Questions2019].
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Frequently voiced concerns are about increasing work, thereby lengthening projects and restricting researcher's freedom by confining them to their predefined plan. However, preplanning simply reorders the workflow rather than creating extra work, potentially preventing costly redesigns or follow-up-studies. Additionally, this does not inhibit exploratory work as the goal is to provide clarity and transparency by distinguishing between preplanned analysis and those conducted after viewing the data. By moving the conceptual work upstream, preregistration clarifies claims, adds transparency to the decision process and strengthens credibility by marking plans and deviations [@loggPreregistrationWeighingCosts2021; @evansImprovingEvidencebasedPractice2023]. In-principle acceptance adds a guarantee to the upfront work, provided the approved plan is followed[@sarafoglouSurveyHowPreregistration2022; @banksAnswers18Questions2019].
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In summary, preregistration does not constrain scientific creativity; it clarifies claims. By making the sequence of decisions explicit-what was planned, what changed, and why-we reduce bias, improve interpretability, and strengthen confidence in reported findings [@hardwickeReducingBiasIncreasing2023].
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In summary, preregistration does not constrain scientific creativity; it clarifies claims. By making the sequence of decisions explicit-what was planned, what changed, and why-we reduce bias, improve interpretability, and strengthen confidence in reported findings [@hardwickeReducingBiasIncreasing2023].
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longtable = FALSE, # avoid longtable entirely
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longtable = FALSE, # avoid longtable entirely
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col.names = c("Step #", "Description", "Before", "After", "Dropped"))
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col.names = c("Step #", "Description", "Before", "After", "Dropped"))
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} else {
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} else {
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print("Table: Cases Dropped from all Publications Obtained")
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}
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}
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if (isTRUE(debug_mode)) {
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debug_info[[knitr::opts_current$get("label")]] <-
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debug_info[[knitr::opts_current$get("label")]] <-
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### Full Text Retrieval
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### Full Text Retrieval
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The initial approach to gathering full texts, which used Zotero to translate DOIs as per Scoggins and Robertson, was unreliable across multiple attempts and versions. Due to the unsuitability of existing software tools-either for technical or legal reasons-a custom web application was developed.
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The initial approach to gathering full texts, which used Zotero to translate DOIs as per Scoggins and Robertson, was unreliable across multiple attempts and versions. Due to the unsuitability of existing software tools - either for technical or legal reasons - a custom web application was developed.
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Legal aspects were carefully considered throughout the development. Within the EU, scraping is legal for scientific purposes [@urhg-60d-tdm], but institutional contracts can override this. Scraping was therefore limited to the university network and only to publishers that permit it while other publishers were scraped outside of the network. Technical details are available in the documents provided while the scraper might be made publicly available in the future.
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Legal aspects were carefully considered throughout the development. Within the EU, scraping is legal for scientific purposes [@urhg-60d-tdm], but institutional contracts can override this. Scraping was therefore limited to the university network and only to publishers that permit it while other publishers were scraped outside of the network. Technical details are available in the documents provided while the scraper might be made publicly available in the future.
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Downloading the analytical sample was mostly successful, though some publisher protections caused dropouts. Due to time constraints, additional runs were not feasible. Documents under 1,000 words were considered non-full-text papers. However, shorter HTML texts were retained for potential keyword matching. Text quality assessment (Flesch-Index) and word count identified missing full texts [@benoitQuantedaPackageQuantitative2018], with further analysis available in the methodological report. Full texts were downloaded for Independent Sample A and the Analytical Sample from which Sample B was drawn. The resulting dropouts should have been implicitly handled by post-stratification. Publisher-level weighting was considered but infeasible due to sparse cells that would have produced unstable weights. Post-stratification was conducted by year only, which does not correct publisher- or journal-specific dropouts. Future iterations should add publisher-level adjustment.
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Downloading the analytical sample was mostly successful, though some publisher protections caused dropouts. Due to time constraints, additional more optimized runs were not feasible. Documents under 1,000 words were considered non-full-text papers. However, shorter HTML texts were retained for potential keyword matching. Text quality assessment (Flesch-Index) and word count identified missing full texts [@benoitQuantedaPackageQuantitative2018], with further analysis available in the methodological report. Full texts were downloaded for Independent Sample A and the Analytical Sample from which Sample B was drawn. The resulting dropouts should have been implicitly handled by post-stratification. Publisher-level weighting was considered but infeasible due to sparse cells that would have produced unstable weights. Post-stratification was conducted by year only, which does not correct publisher- or journal-specific dropouts. Future iterations should add publisher-level adjustment.
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```{r}
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```{r}
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#| label: tbl-cases2
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#| label: tbl-cases2
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print("Table: Cases Dropped from Analytical Sample")
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Further instructions can be found in the README file. Full-text data and the downloader can't be made available to the public due to copyright concerns. An encrypted, password-protected file for each containing the full-texts is available in the repository.
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Further instructions can be found in the README file. Full-text data and the downloader can't be made available to the public due to copyright concerns. An encrypted, password-protected file for each containing the full-texts is available in the repository.
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# Acknowlegments
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# Funding
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This research received no external funding.
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# Conflicts of Interest
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The authors declare no conflicts of interest.
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```{=latex}
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```{=latex}
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\newpage
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\newpage
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```
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```
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Reference in New Issue
Block a user